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Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and
  Robust Convergence Without the Condition Number
v1v2 (latest)

Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number

26 October 2020
Tian Tong
Cong Ma
Yuejie Chi
ArXiv (abs)PDFHTML

Papers citing "Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number"

15 / 15 papers shown
Title
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
G. Zhang
Salar Fattahi
Richard Y. Zhang
144
37
0
13 Apr 2025
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
Paris Giampouras
HanQin Cai
René Vidal
143
4
0
22 Oct 2024
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition
Zhen Qin
Zhihui Zhu
212
5
0
10 Jun 2024
Computationally Efficient and Statistically Optimal Robust
  High-Dimensional Linear Regression
Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression
Yinan Shen
Jingyang Li
Jian-Feng Cai
Dong Xia
59
1
0
10 May 2023
The Power of Preconditioning in Overparameterized Low-Rank Matrix
  Sensing
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
Xingyu Xu
Yandi Shen
Yuejie Chi
Cong Ma
122
37
0
02 Feb 2023
Deep Unfolded Tensor Robust PCA with Self-supervised Learning
Deep Unfolded Tensor Robust PCA with Self-supervised Learning
Harry Dong
Megna Shah
S. Donegan
Yuejie Chi
SSL
88
6
0
21 Dec 2022
Unrolling SVT to obtain computationally efficient SVT for n-qubit
  quantum state tomography
Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography
S. Shanmugam
Sheetal Kalyani
50
7
0
17 Dec 2022
Fast and Provable Tensor Robust Principal Component Analysis via Scaled
  Gradient Descent
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
Harry Dong
Tian Tong
Cong Ma
Yuejie Chi
89
14
0
18 Jun 2022
Nonconvex Factorization and Manifold Formulations are Almost Equivalent
  in Low-rank Matrix Optimization
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
Yuetian Luo
Xudong Li
Anru R. Zhang
81
11
0
03 Aug 2021
GNMR: A provable one-line algorithm for low rank matrix recovery
GNMR: A provable one-line algorithm for low rank matrix recovery
Pini Zilber
B. Nadler
133
14
0
24 Jun 2021
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation
  from Incomplete Measurements
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
Tian Tong
Cong Ma
Ashley Prater-Bennette
Erin E. Tripp
Yuejie Chi
108
33
0
29 Apr 2021
Spectral Methods for Data Science: A Statistical Perspective
Spectral Methods for Data Science: A Statistical Perspective
Yuxin Chen
Yuejie Chi
Jianqing Fan
Cong Ma
153
173
0
15 Dec 2020
Recursive Importance Sketching for Rank Constrained Least Squares:
  Algorithms and High-order Convergence
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence
Yuetian Luo
Wen Huang
Xudong Li
Anru R. Zhang
76
16
0
17 Nov 2020
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled
  Gradient Descent
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Tian Tong
Cong Ma
Yuejie Chi
104
120
0
18 May 2020
Nonconvex Matrix Factorization from Rank-One Measurements
Nonconvex Matrix Factorization from Rank-One Measurements
Yuanxin Li
Cong Ma
Yuxin Chen
Yuejie Chi
70
51
0
17 Feb 2018
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